
"""simple docstring"""


import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 	__a	(   snake_case_						):
       UpperCamelCase_		:       torch.FloatTensor
       UpperCamelCase_		:       Optional[torch.FloatTensor]				=      None





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_=0.999		,   UpperCAmelCase_="cosine"		,   )->				Optional[Any]:







       """simple docstring"""
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(UpperCAmelCase_					):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(UpperCAmelCase_					):
                     return math.exp(t * -12.0					)

       else:
              raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"					)

       UpperCamelCase       			=		[]
       for i in range(UpperCAmelCase_					):
              UpperCamelCase       			=		i / num_diffusion_timesteps
              UpperCamelCase       			=		(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_					) / alpha_bar_fn(UpperCAmelCase_					)		,   UpperCAmelCase_					)					)
       return torch.tensor(UpperCAmelCase_		,   dtype=torch.floataa					)



class 	__a	(   snake_case_     ,  snake_case_						):
       UpperCamelCase_		:       str				=      1






       @register_to_config
       def __init__(	self			:				Tuple							,   UpperCAmelCase_			:				int = 1_000							,   UpperCAmelCase_			:				float = 0.0001							,   UpperCAmelCase_			:				float = 0.02							,   UpperCAmelCase_			:				str = "linear"							,   UpperCAmelCase_			:				Optional[Union[np.ndarray, List[float]]] = None							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				str = "epsilon"							,   UpperCAmelCase_			:				float = 1.0							,   **UpperCAmelCase_			:				Any							,   )->	Optional[int]:







              """simple docstring"""
              if kwargs.get("set_alpha_to_one"							,   lowerCAmelCase__      ) is not None:
                     UpperCamelCase       			=		(
                         '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
                     )
                     deprecate("set_alpha_to_one"							,   "1.0.0"							,   lowerCAmelCase__							,   standard_warn=lowerCAmelCase__      )
                     UpperCamelCase       			=		kwargs['''set_alpha_to_one''']
              if trained_betas is not None:
                     UpperCamelCase       			=		torch.tensor(lowerCAmelCase__							,   dtype=torch.floataa      )
              elif beta_schedule == "linear":
                     UpperCamelCase       			=		torch.linspace(lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   dtype=torch.floataa      )
              elif beta_schedule == "scaled_linear":
                     # this schedule is very specific to the latent diffusion model.
                     UpperCamelCase       			=		(
                         torch.linspace(beta_start**0.5							,   beta_end**0.5							,   lowerCAmelCase__							,   dtype=torch.floataa      ) ** 2
                     )
              elif beta_schedule == "squaredcos_cap_v2":
                     # Glide cosine schedule
                     UpperCamelCase       			=		betas_for_alpha_bar(lowerCAmelCase__      )
              else:
                     raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"      )

              UpperCamelCase       			=		1.0 - self.betas
              UpperCamelCase       			=		torch.cumprod(self.alphas							,   dim=0      )

              # At every step in inverted ddim, we are looking into the next alphas_cumprod
              # For the final step, there is no next alphas_cumprod, and the index is out of bounds
              # `set_alpha_to_zero` decides whether we set this parameter simply to zero
              # in this case, self.step() just output the predicted noise
              # or whether we use the final alpha of the "non-previous" one.
              UpperCamelCase       			=		torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

              # standard deviation of the initial noise distribution
              UpperCamelCase       			=		1.0

              # setable values
              UpperCamelCase       			=		None
              UpperCamelCase       			=		torch.from_numpy(np.arange(0							,   lowerCAmelCase__      ).copy().astype(np.intaa      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				Optional[int] = None      )->	Dict:







              """simple docstring"""
              return sample






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Union[str, torch.device] = None      )->	Optional[int]:







              """simple docstring"""

              if num_inference_steps > self.config.num_train_timesteps:
                     raise ValueError(
                         f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
                         f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
                         f" maximal {self.config.num_train_timesteps} timesteps."      )

              UpperCamelCase       			=		num_inference_steps
              UpperCamelCase       			=		self.config.num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              UpperCamelCase       			=		(np.arange(0							,   lowerCAmelCase__      ) * step_ratio).round().copy().astype(np.intaa      )
              UpperCamelCase       			=		torch.from_numpy(lowerCAmelCase__      ).to(lowerCAmelCase__      )
              self.timesteps += self.config.steps_offset






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				torch.FloatTensor							,   UpperCAmelCase_			:				float = 0.0							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[torch.FloatTensor] = None							,   UpperCAmelCase_			:				bool = True							,   )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		timestep + self.config.num_train_timesteps // self.num_inference_steps

              # 2. compute alphas, betas
              # change original implementation to exactly match noise levels for analogous forward process
              UpperCamelCase       			=		self.alphas_cumprod[timestep]
              UpperCamelCase       			=		(
                  self.alphas_cumprod[prev_timestep]
                  if prev_timestep < self.config.num_train_timesteps
                  else self.final_alpha_cumprod
              )

              UpperCamelCase       			=		1 - alpha_prod_t

              # 3. compute predicted original sample from predicted noise also called
              # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
              if self.config.prediction_type == "epsilon":
                     UpperCamelCase       			=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                     UpperCamelCase       			=		model_output
              elif self.config.prediction_type == "sample":
                     UpperCamelCase       			=		model_output
                     UpperCamelCase       			=		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
              elif self.config.prediction_type == "v_prediction":
                     UpperCamelCase       			=		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                     UpperCamelCase       			=		(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
              else:
                     raise ValueError(
                         f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                         " `v_prediction`"      )

              # 4. Clip or threshold "predicted x_0"
              if self.config.clip_sample:
                     UpperCamelCase       			=		pred_original_sample.clamp(
                         -self.config.clip_sample_range							,   self.config.clip_sample_range      )

              # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
              UpperCamelCase       			=		(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

              # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
              UpperCamelCase       			=		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

              if not return_dict:
                     return (prev_sample, pred_original_sample)
              return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__							,   pred_original_sample=lowerCAmelCase__      )






       def __len__(	self			:				Any      )->	int:







              """simple docstring"""
              return self.config.num_train_timesteps










'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np
def        UpperCAmelCase (    A	:					dict       ,		A	:					str       ,		A	:					set       ,		A	:					set       ,		A	:					dict       ,		A	:					dict       ,		A	:					PriorityQueue       ,		A	:					dict       ,		A	:					float | int       ,		):
							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE       :   str         =      cst_fwd.get(A       ,		np.inf					)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      new_cost_f
																					SCREAMING_SNAKE_CASE       :   List[str]         =      v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					dict       ,		A	:					dict					):
							SCREAMING_SNAKE_CASE       :   Dict         =      -1

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      set()
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      set()
							SCREAMING_SNAKE_CASE       :   Any         =      {source: 0}
							SCREAMING_SNAKE_CASE       :   Tuple         =      {destination: 0}
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {source: None}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      {destination: None}
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()

							SCREAMING_SNAKE_CASE       :   List[str]         =      np.inf

							queue_forward.put((0, source)					)
							queue_backward.put((0, destination)					)

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      queue_forward.get()
														visited_forward.add(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      queue_backward.get()
														visited_backward.add(A					)

														SCREAMING_SNAKE_CASE       :   Tuple         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE       :   int         =      shortest_distance
							return shortest_path_distance


lowerCAmelCase_  :							int             =    {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCAmelCase_  :							str             =    {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
							import doctest

							doctest.testmod()




